A virtual manikin is a computer-generated image of an entity (i.e., a person or thing) that can be manipulated to assume a position or pose an actual person or thing could naturally assume. For the embodiments described herein, the entity is a human body or a portion of a human body, although in general the describe concepts could be applied to other entities, for example real animals or imaginary creatures.
A virtual manikin may include a simplified upper limb kinematic chain that consists of four segments: a clavicle, a humerus, a forearm and a hand. This kinematic chain may include, for example, nine degrees of freedom (DOF), allowing the end-effector of the chain (i.e., the hand) to be moved to engage a specified target such as a handle, a drill or a screwdriver.
The kinematic chain may also include a range of motion (ROM) in terms of spatial bounds for each DOF of the kinematic chain. This way of simulating the human upper limb is similar to what is seen in robot manipulators, for which nothing more than constant lower and upper bounds on each DOF are necessary to limit the movements of a redundant kinematic chain.
Virtual manikins often use an inverse kinematic (IK) posturing engine to find the configuration of the manikin (i.e., the value of each DOF) that positions the end-effector on the specified target. Although the IK posturing engine is quite efficient in finding a solution, the predicted postures lack robustness. A “lack of robustness” refers to the fact that a slight displacement of the target may lead to abrupt jumps in the DOF angles, which may result in an unrealistic human posture.
This lack of robustness is due, at least in part, to the redundancy of the kinematic chain. Kinematic chain redundancy refers to the fact that the kinematic chain can accommodate multiple ways to accomplish the same end-effector position. The redundancy exists because of a high number of DOFs (in this example, nine DOFs from the clavicle to the hand), with respect to the number of end-effector constraints (between 3 and 6 for the hand, for this example).
It is known that physiological dependencies exist between various DOFs of the human upper limb. For example, when the humerus is elevated in different planes of elevation, the clavicle and scapula segments follow a repeatable, non-linear pattern of motion, known as the “shoulder rhythm.” Further, a non-linear relationship exists between the orientation of the humerus in space, and the limit of internal/external humerus axial rotation.
Among others, these physiological dependencies represent the complex physiological nature of the human upper limb, which is far more sophisticated than a robot manipulator. A complete integration of all these physiological dependencies is lacking in conventional virtual manikins found in the art.
The upper limb of virtual manikins known in the art thus involves (1) a redundancy in the kinematic chain, (2) a simplified kinematic chain that does not take into account the dependencies between each DOF, and (3) a simplification and (sometimes) under/overestimation of ROM that come from outdated databases. The direct consequence is a solution space that is too large, which may lead to unwanted oscillation in the kinematic chain and poor robustness of the predicted posture.
From a user's perspective, posturing the virtual manikin with a conventional upper limb model can become a challenging and cumbersome task. The posturing task may require a substantial amount of time, reducing the time available for biomechanical analysis of the resulting posture. Moreover, with such model, the users have less confidence in the predicted postures, given that some postures may seem impossible to achieve in reality.